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In Robotics, especially in this era of autonomous driving, mapping is one keyability of a robot to be able to navigate through an environment, localize onit and analyze its traversability. To allow for real-time execution onconstrained hardware, the map usually estimated by feature-based or semi-denseSLAM algorithms is a sparse point cloud; a richer and more completerepresentation of the environment is desirable. Existing dense mappingalgorithms require extensive use of GPU computing and they hardly scale tolarge environments; incremental algorithms from sparse points still representan effective solution when light computational effort is needed and bigsequences have to be processed in real-time. In this paper we improved andextended the state of the art incremental manifold mesh algorithm proposed in[1] and extended in [2]. While these algorithms do not achieve real-time andthey embed points from SLAM or Structure from Motion only when their positionis fixed, in this paper we propose the first incremental algorithm able toreconstruct a manifold mesh in real-time through single core CPU processingwhich is also able to modify the mesh according to 3D points updates from theunderlying SLAM algorithm. We tested our algorithm against two state of the artincremental mesh mapping systems on the KITTI dataset, and we showed that,while accuracy is comparable, our approach is able to reach real-timeperformances thanks to an order of magnitude speed-up.

Real-time CPU-based large-scale 3D mesh reconstruction